Career Lab · November 2026

Job switch first.
ML growth in parallel.

An 8-week action system for landing the next Data Scientist role. Prioritize relevant DS interviews, H-1B continuity, and evidence-based skill development. No Data Analyst fallback track.

8 weeksNov 2 – Dec 27

0 / 0Tasks complete

~10h/weekFlexible suggested capacity

Job search · 60%

Target DS in Ads/Marketing, Product/Experimentation, and Applied Search/Ranking. Referrals, thoughtful applications, résumé iteration, and project narratives.

Interview + ML · 40%

Two hours SQL/Python, 2.5 hours stats/ML, 1.5 hours project stories (adjust if interviews arrive). Deep learning specialization stays on the parallel page.

Decision rules

Do not wait for 'complete ML mastery' to apply.
Prioritize real DS responsibilities, employer stability and confirmed visa support.
Never resign solely on the basis of a verbal offer. Ask immigration counsel about any employment-status changes.
Distinguish confirmed interview gaps from imagined gaps.

Weekly action plan

Week 1 · Positioning

Nov 2–8, 2026

Primary: job switch. ML practice stays intentionally small.

Finalize DS résumé focused on Amazon Ads, ranking and measurable ownershipWrite 2-minute and 8-minute versions of both flagship projectsReach out to 3–5 trusted contacts for DS referralsApply to 5–8 strongly matched DS rolesConfirm H-1B transfer, PERM and planned travel questions with immigration counsel

Week 2 · Launch

Nov 9–15

Primary: job switch. ML practice stays intentionally small.

Apply to 10–15 targeted DS roles; record company, job link and application datePractice SQL twice (joins, windows, cohort funnels)Practice ML diagnostics once: leakage vs overfitting vs shiftPractice one project storytelling mock interviewReview replies, sponsorship indications and résumé conversion

Week 3 · Diagnose

Nov 16–22

Primary: job switch. ML practice stays intentionally small.

Continue 10–15 quality DS applications and referral follow-upsDo one stats/A-B test case and one Python coding exerciseRun first full DS mock interview; write 3 concrete gapsImprove Ads project story: objective, constraints, baseline, confidenceReview employer stability and visa timeline with documented facts

Week 4 · Iterate

Nov 23–29

Primary: job switch. ML practice stays intentionally small.

Follow up on applications and add 10–15 targeted rolesRehearse ranking evaluation: NDCG, leakage, online A/BRehearse advertising metrics: ROAS, ACOS, attribution, counterfactual concernsUpdate résumé bullets based on recruiter responseSummarize learnings and adjust target job titles

Week 5 · Deepen

Nov 30–Dec 6

Primary: job switch. ML practice stays intentionally small.

Prioritize live interview loops and interviewer-specific preparationPractice experimental design and power/sample-size reasoningBuild one reproducible ML mini-experiment (offline only)Continue targeted DS outreach and sponsorship checks

Week 6 · Expand selectively

Dec 7–13

Primary: job switch. ML practice stays intentionally small.

Evaluate application-to-screen and screen-to-next-round ratesTarget adjacent DS specializations where experience is relevantPractice Python/SQL under timed conditionsPrepare behavioral stories around ambiguity, collaboration and tradeoffs

Week 7 · Interview conversion

Dec 14–20

Primary: job switch. ML practice stays intentionally small.

Focus on upcoming interviews and recruiter communicationPractice one full ML system design for Ads RankingPrepare precise project ownership and results evidenceRefresh immigration contingency checklist with counsel

Week 8 · Review and reset

Dec 21–27

Primary: job switch. ML practice stays intentionally small.

Review all applications and interview-stage losses without self-judgmentIdentify top 3 interview gaps supported by actual feedbackAdjust January target roles and learning focusCelebrate demonstrated improvements and set next 4-week sprint

Weekly journal

Capture outreach, interview signal, what you practiced and what to change next week.